Overview

Dataset statistics

Number of variables13
Number of observations2774
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory281.9 KiB
Average record size in memory104.0 B

Variable types

Numeric13

Alerts

gross_revenue is highly correlated with qtt_invoices and 3 other fieldsHigh correlation
qtt_invoices is highly correlated with gross_revenue and 2 other fieldsHigh correlation
unique_products is highly correlated with gross_revenue and 3 other fieldsHigh correlation
total_products is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_ticket is highly correlated with avg_unique_basket_sizeHigh correlation
avg_recency is highly correlated with daily_purchase_rateHigh correlation
daily_purchase_rate is highly correlated with avg_recencyHigh correlation
avg_basket_size is highly correlated with gross_revenue and 1 other fieldsHigh correlation
avg_unique_basket_size is highly correlated with unique_products and 1 other fieldsHigh correlation
gross_revenue is highly correlated with qtt_invoices and 1 other fieldsHigh correlation
qtt_invoices is highly correlated with gross_revenue and 2 other fieldsHigh correlation
unique_products is highly correlated with qtt_invoices and 1 other fieldsHigh correlation
total_products is highly correlated with gross_revenue and 1 other fieldsHigh correlation
avg_ticket is highly correlated with total_prod_returned and 1 other fieldsHigh correlation
total_prod_returned is highly correlated with avg_ticket and 1 other fieldsHigh correlation
avg_basket_size is highly correlated with avg_ticket and 1 other fieldsHigh correlation
avg_unique_basket_size is highly correlated with unique_productsHigh correlation
gross_revenue is highly correlated with qtt_invoices and 1 other fieldsHigh correlation
qtt_invoices is highly correlated with gross_revenue and 1 other fieldsHigh correlation
unique_products is highly correlated with avg_unique_basket_sizeHigh correlation
total_products is highly correlated with gross_revenue and 2 other fieldsHigh correlation
avg_recency is highly correlated with daily_purchase_rateHigh correlation
daily_purchase_rate is highly correlated with avg_recencyHigh correlation
avg_basket_size is highly correlated with total_productsHigh correlation
avg_unique_basket_size is highly correlated with unique_productsHigh correlation
df_index is highly correlated with avg_recencyHigh correlation
gross_revenue is highly correlated with qtt_invoices and 5 other fieldsHigh correlation
qtt_invoices is highly correlated with gross_revenue and 2 other fieldsHigh correlation
unique_products is highly correlated with gross_revenue and 3 other fieldsHigh correlation
total_products is highly correlated with gross_revenue and 5 other fieldsHigh correlation
avg_ticket is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_recency is highly correlated with df_indexHigh correlation
total_prod_returned is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_basket_size is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_unique_basket_size is highly correlated with unique_productsHigh correlation
avg_ticket is highly skewed (γ1 = 51.90076423) Skewed
daily_purchase_rate is highly skewed (γ1 = 46.08539806) Skewed
total_prod_returned is highly skewed (γ1 = 50.10197766) Skewed
avg_basket_size is highly skewed (γ1 = 44.86093386) Skewed
df_index has unique values Unique
customer_id has unique values Unique
recency_days has 34 (1.2%) zeros Zeros
total_prod_returned has 1481 (53.4%) zeros Zeros

Reproduction

Analysis started2022-08-07 11:44:35.662265
Analysis finished2022-08-07 11:45:26.355241
Duration50.69 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct2774
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2251.237203
Minimum0
Maximum5696
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2022-08-07T08:45:26.565849image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile181.65
Q1901.5
median2061.5
Q33411.25
95-th percentile4958.85
Maximum5696
Range5696
Interquartile range (IQR)2509.75

Descriptive statistics

Standard deviation1526.597887
Coefficient of variation (CV)0.6781150763
Kurtosis-0.956310095
Mean2251.237203
Median Absolute Deviation (MAD)1241
Skewness0.3794934938
Sum6244932
Variance2330501.11
MonotonicityStrictly increasing
2022-08-07T08:45:26.859451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
29101
 
< 0.1%
28961
 
< 0.1%
28971
 
< 0.1%
29001
 
< 0.1%
29011
 
< 0.1%
29051
 
< 0.1%
29061
 
< 0.1%
29071
 
< 0.1%
29081
 
< 0.1%
Other values (2764)2764
99.6%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
56961
< 0.1%
56861
< 0.1%
56801
< 0.1%
56551
< 0.1%
56491
< 0.1%
56381
< 0.1%
56371
< 0.1%
56211
< 0.1%
56201
< 0.1%
56111
< 0.1%

customer_id
Real number (ℝ≥0)

UNIQUE

Distinct2774
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15285.69971
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2022-08-07T08:45:27.167348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12626.65
Q113815.25
median15242.5
Q316779.75
95-th percentile17950.35
Maximum18287
Range5940
Interquartile range (IQR)2964.5

Descriptive statistics

Standard deviation1714.984904
Coefficient of variation (CV)0.1121953811
Kurtosis-1.206915065
Mean15285.69971
Median Absolute Deviation (MAD)1483.5
Skewness0.01599078757
Sum42402531
Variance2941173.222
MonotonicityNot monotonic
2022-08-07T08:45:27.442532image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178501
 
< 0.1%
144821
 
< 0.1%
170581
 
< 0.1%
177041
 
< 0.1%
169331
 
< 0.1%
137721
 
< 0.1%
162491
 
< 0.1%
141981
 
< 0.1%
139891
 
< 0.1%
179301
 
< 0.1%
Other values (2764)2764
99.6%
ValueCountFrequency (%)
123471
< 0.1%
123481
< 0.1%
123521
< 0.1%
123561
< 0.1%
123581
< 0.1%
123591
< 0.1%
123601
< 0.1%
123621
< 0.1%
123641
< 0.1%
123701
< 0.1%
ValueCountFrequency (%)
182871
< 0.1%
182831
< 0.1%
182821
< 0.1%
182731
< 0.1%
182721
< 0.1%
182701
< 0.1%
182651
< 0.1%
182631
< 0.1%
182611
< 0.1%
182601
< 0.1%

gross_revenue
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2757
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2904.751532
Minimum36.56
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2022-08-07T08:45:27.786035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum36.56
5-th percentile264.557
Q1628.9125
median1170.87
Q32424.715
95-th percentile7579.4915
Maximum279138.02
Range279101.46
Interquartile range (IQR)1795.8025

Descriptive statistics

Standard deviation10927.21927
Coefficient of variation (CV)3.761843017
Kurtosis331.9508666
Mean2904.751532
Median Absolute Deviation (MAD)688.765
Skewness16.26093044
Sum8057780.75
Variance119404120.9
MonotonicityNot monotonic
2022-08-07T08:45:28.099311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
889.932
 
0.1%
1066.152
 
0.1%
1353.742
 
0.1%
1314.452
 
0.1%
598.22
 
0.1%
731.92
 
0.1%
2053.022
 
0.1%
3312
 
0.1%
734.942
 
0.1%
178.962
 
0.1%
Other values (2747)2754
99.3%
ValueCountFrequency (%)
36.561
< 0.1%
521
< 0.1%
52.21
< 0.1%
62.431
< 0.1%
68.841
< 0.1%
70.021
< 0.1%
77.41
< 0.1%
84.651
< 0.1%
90.31
< 0.1%
93.351
< 0.1%
ValueCountFrequency (%)
279138.021
< 0.1%
259657.31
< 0.1%
194550.791
< 0.1%
168472.51
< 0.1%
140450.721
< 0.1%
124564.531
< 0.1%
117379.631
< 0.1%
91062.381
< 0.1%
72882.091
< 0.1%
66653.561
< 0.1%

recency_days
Real number (ℝ≥0)

ZEROS

Distinct252
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.62689257
Minimum0
Maximum372
Zeros34
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2022-08-07T08:45:28.487788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q110
median29
Q373
95-th percentile211
Maximum372
Range372
Interquartile range (IQR)63

Descriptive statistics

Standard deviation68.41964137
Coefficient of variation (CV)1.208253504
Kurtosis3.432018391
Mean56.62689257
Median Absolute Deviation (MAD)23.5
Skewness1.898344739
Sum157083
Variance4681.247326
MonotonicityNot monotonic
2022-08-07T08:45:28.803273image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199
 
3.6%
487
 
3.1%
285
 
3.1%
385
 
3.1%
876
 
2.7%
1067
 
2.4%
966
 
2.4%
765
 
2.3%
1762
 
2.2%
2255
 
2.0%
Other values (242)2027
73.1%
ValueCountFrequency (%)
034
 
1.2%
199
3.6%
285
3.1%
385
3.1%
487
3.1%
543
1.6%
765
2.3%
876
2.7%
966
2.4%
1067
2.4%
ValueCountFrequency (%)
3721
 
< 0.1%
3661
 
< 0.1%
3601
 
< 0.1%
3583
0.1%
3541
 
< 0.1%
3371
 
< 0.1%
3362
0.1%
3341
 
< 0.1%
3332
0.1%
3301
 
< 0.1%

qtt_invoices
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct55
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.053352559
Minimum2
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2022-08-07T08:45:29.146948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median4
Q36
95-th percentile17
Maximum206
Range204
Interquartile range (IQR)4

Descriptive statistics

Standard deviation9.071461768
Coefficient of variation (CV)1.498584739
Kurtosis183.9551027
Mean6.053352559
Median Absolute Deviation (MAD)2
Skewness10.62505905
Sum16792
Variance82.29141862
MonotonicityNot monotonic
2022-08-07T08:45:29.465670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2780
28.1%
3499
18.0%
4393
14.2%
5237
 
8.5%
6173
 
6.2%
7138
 
5.0%
898
 
3.5%
969
 
2.5%
1055
 
2.0%
1154
 
1.9%
Other values (45)278
 
10.0%
ValueCountFrequency (%)
2780
28.1%
3499
18.0%
4393
14.2%
5237
 
8.5%
6173
 
6.2%
7138
 
5.0%
898
 
3.5%
969
 
2.5%
1055
 
2.0%
1154
 
1.9%
ValueCountFrequency (%)
2061
< 0.1%
1991
< 0.1%
1241
< 0.1%
971
< 0.1%
912
0.1%
861
< 0.1%
721
< 0.1%
622
0.1%
601
< 0.1%
571
< 0.1%

unique_products
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct340
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.30425379
Minimum1
Maximum1786
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2022-08-07T08:45:29.765711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q129
median57
Q3105
95-th percentile239.35
Maximum1786
Range1785
Interquartile range (IQR)76

Descriptive statistics

Standard deviation98.73614828
Coefficient of variation (CV)1.185247377
Kurtosis80.59983574
Mean83.30425379
Median Absolute Deviation (MAD)33
Skewness6.350556843
Sum231086
Variance9748.826978
MonotonicityNot monotonic
2022-08-07T08:45:30.040249image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3738
 
1.4%
2437
 
1.3%
2636
 
1.3%
3335
 
1.3%
2534
 
1.2%
2834
 
1.2%
1832
 
1.2%
3032
 
1.2%
1530
 
1.1%
3129
 
1.0%
Other values (330)2437
87.9%
ValueCountFrequency (%)
119
0.7%
213
0.5%
318
0.6%
418
0.6%
523
0.8%
619
0.7%
721
0.8%
824
0.9%
923
0.8%
1020
0.7%
ValueCountFrequency (%)
17861
< 0.1%
17661
< 0.1%
13221
< 0.1%
11181
< 0.1%
8841
< 0.1%
8171
< 0.1%
7171
< 0.1%
7141
< 0.1%
6991
< 0.1%
6361
< 0.1%

total_products
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1639
Distinct (%)59.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1700.379957
Minimum2
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2022-08-07T08:45:30.344348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile119.65
Q1330.25
median705.5
Q31478.75
95-th percentile4645.5
Maximum196844
Range196842
Interquartile range (IQR)1148.5

Descriptive statistics

Standard deviation6079.161482
Coefficient of variation (CV)3.575178276
Kurtosis437.6447231
Mean1700.379957
Median Absolute Deviation (MAD)453.5
Skewness17.32001834
Sum4716854
Variance36956204.33
MonotonicityNot monotonic
2022-08-07T08:45:30.711016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31011
 
0.4%
2468
 
0.3%
1508
 
0.3%
2197
 
0.3%
2007
 
0.3%
3007
 
0.3%
4937
 
0.3%
12007
 
0.3%
2727
 
0.3%
2607
 
0.3%
Other values (1629)2698
97.3%
ValueCountFrequency (%)
21
< 0.1%
161
< 0.1%
171
< 0.1%
191
< 0.1%
201
< 0.1%
251
< 0.1%
272
0.1%
301
< 0.1%
321
< 0.1%
332
0.1%
ValueCountFrequency (%)
1968441
< 0.1%
809971
< 0.1%
802631
< 0.1%
773731
< 0.1%
699931
< 0.1%
645491
< 0.1%
641241
< 0.1%
633121
< 0.1%
583431
< 0.1%
578851
< 0.1%

avg_ticket
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct2772
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.33677308
Minimum2.150588235
Maximum56157.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2022-08-07T08:45:31.114179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2.150588235
5-th percentile4.852702153
Q112.42379049
median17.94212763
Q325.07465812
95-th percentile88.42744262
Maximum56157.5
Range56155.34941
Interquartile range (IQR)12.65086763

Descriptive statistics

Standard deviation1071.049203
Coefficient of variation (CV)20.46456325
Kurtosis2718.321218
Mean52.33677308
Median Absolute Deviation (MAD)6.338589039
Skewness51.90076423
Sum145182.2085
Variance1147146.395
MonotonicityNot monotonic
2022-08-07T08:45:31.413711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.478333332
 
0.1%
4.1622
 
0.1%
6.2697008551
 
< 0.1%
32.597751
 
< 0.1%
19.030483871
 
< 0.1%
28.554516131
 
< 0.1%
12.800681821
 
< 0.1%
6.3962146891
 
< 0.1%
26.087971011
 
< 0.1%
17.984615381
 
< 0.1%
Other values (2762)2762
99.6%
ValueCountFrequency (%)
2.1505882351
< 0.1%
2.43251
< 0.1%
2.4623711341
< 0.1%
2.5112413791
< 0.1%
2.5153333331
< 0.1%
2.651
< 0.1%
2.6569318181
< 0.1%
2.7075982531
< 0.1%
2.7606215721
< 0.1%
2.7704641911
< 0.1%
ValueCountFrequency (%)
56157.51
< 0.1%
4453.431
< 0.1%
1687.21
< 0.1%
952.98751
< 0.1%
872.131
< 0.1%
841.02144931
< 0.1%
651.16833331
< 0.1%
6401
< 0.1%
624.41
< 0.1%
615.751
< 0.1%

avg_recency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1155
Distinct (%)41.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.79449884
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2022-08-07T08:45:31.741119image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q134.14930556
median59
Q399
95-th percentile224
Maximum366
Range365
Interquartile range (IQR)64.85069444

Descriptive statistics

Standard deviation66.52001781
Coefficient of variation (CV)0.844221599
Kurtosis3.673385052
Mean78.79449884
Median Absolute Deviation (MAD)30
Skewness1.828126135
Sum218575.9398
Variance4424.912769
MonotonicityNot monotonic
2022-08-07T08:45:32.066949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7021
 
0.8%
4618
 
0.6%
5517
 
0.6%
3116
 
0.6%
9116
 
0.6%
4916
 
0.6%
2115
 
0.5%
4215
 
0.5%
3515
 
0.5%
2614
 
0.5%
Other values (1145)2611
94.1%
ValueCountFrequency (%)
19
0.3%
24
0.1%
2.8615384621
 
< 0.1%
36
0.2%
3.3303571431
 
< 0.1%
3.3513513511
 
< 0.1%
45
0.2%
4.1910112361
 
< 0.1%
4.2758620691
 
< 0.1%
4.51
 
< 0.1%
ValueCountFrequency (%)
3661
 
< 0.1%
3651
 
< 0.1%
3641
 
< 0.1%
3631
 
< 0.1%
3572
0.1%
3561
 
< 0.1%
3552
0.1%
3521
 
< 0.1%
3512
0.1%
3503
0.1%

daily_purchase_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct1225
Distinct (%)44.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04969870057
Minimum0.005449591281
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2022-08-07T08:45:32.409351image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.005449591281
5-th percentile0.008746355685
Q10.01575839204
median0.0243902439
Q30.04166666667
95-th percentile0.1153846154
Maximum17
Range16.99455041
Interquartile range (IQR)0.02590827462

Descriptive statistics

Standard deviation0.337595074
Coefficient of variation (CV)6.792835026
Kurtosis2296.516337
Mean0.04969870057
Median Absolute Deviation (MAD)0.01069454458
Skewness46.08539806
Sum137.8641954
Variance0.113970434
MonotonicityNot monotonic
2022-08-07T08:45:32.691611image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.062518
 
0.6%
0.0277777777817
 
0.6%
0.0238095238116
 
0.6%
0.0833333333315
 
0.5%
0.0909090909115
 
0.5%
0.0294117647114
 
0.5%
0.0344827586214
 
0.5%
0.0192307692313
 
0.5%
0.0256410256413
 
0.5%
0.0212765957413
 
0.5%
Other values (1215)2626
94.7%
ValueCountFrequency (%)
0.0054495912811
 
< 0.1%
0.0054644808741
 
< 0.1%
0.0054794520551
 
< 0.1%
0.0054945054951
 
< 0.1%
0.0055865921792
0.1%
0.0056022408961
 
< 0.1%
0.0056179775282
0.1%
0.005665722381
 
< 0.1%
0.0056818181822
0.1%
0.0056980056983
0.1%
ValueCountFrequency (%)
171
 
< 0.1%
31
 
< 0.1%
21
 
< 0.1%
1.1428571431
 
< 0.1%
18
0.3%
0.751
 
< 0.1%
0.66666666673
 
0.1%
0.5508021391
 
< 0.1%
0.53351206431
 
< 0.1%
0.53
 
0.1%

total_prod_returned
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct205
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.15897621
Minimum0
Maximum80995
Zeros1481
Zeros (%)53.4%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2022-08-07T08:45:33.045062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q39
95-th percentile98
Maximum80995
Range80995
Interquartile range (IQR)9

Descriptive statistics

Standard deviation1564.393524
Coefficient of variation (CV)24.38308116
Kurtosis2586.254065
Mean64.15897621
Median Absolute Deviation (MAD)0
Skewness50.10197766
Sum177977
Variance2447327.097
MonotonicityNot monotonic
2022-08-07T08:45:33.368137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01481
53.4%
1129
 
4.7%
2117
 
4.2%
382
 
3.0%
472
 
2.6%
663
 
2.3%
555
 
2.0%
1245
 
1.6%
839
 
1.4%
938
 
1.4%
Other values (195)653
23.5%
ValueCountFrequency (%)
01481
53.4%
1129
 
4.7%
2117
 
4.2%
382
 
3.0%
472
 
2.6%
555
 
2.0%
663
 
2.3%
738
 
1.4%
839
 
1.4%
938
 
1.4%
ValueCountFrequency (%)
809951
< 0.1%
90141
< 0.1%
80041
< 0.1%
44271
< 0.1%
37681
< 0.1%
33321
< 0.1%
28781
< 0.1%
20221
< 0.1%
20121
< 0.1%
17761
< 0.1%

avg_basket_size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct1938
Distinct (%)69.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean245.961992
Minimum1
Maximum40498.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2022-08-07T08:45:33.853212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile45
Q1103.3333333
median172.125
Q3278.2375
95-th percentile587.875
Maximum40498.5
Range40497.5
Interquartile range (IQR)174.9041667

Descriptive statistics

Standard deviation808.0807949
Coefficient of variation (CV)3.285388887
Kurtosis2223.352169
Mean245.961992
Median Absolute Deviation (MAD)81.29166667
Skewness44.86093386
Sum682298.5657
Variance652994.5711
MonotonicityNot monotonic
2022-08-07T08:45:34.110589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10011
 
0.4%
869
 
0.3%
758
 
0.3%
608
 
0.3%
2087
 
0.3%
1057
 
0.3%
827
 
0.3%
737
 
0.3%
1367
 
0.3%
1977
 
0.3%
Other values (1928)2696
97.2%
ValueCountFrequency (%)
11
< 0.1%
3.3333333331
< 0.1%
5.3333333331
< 0.1%
5.6666666671
< 0.1%
6.1428571431
< 0.1%
7.51
< 0.1%
91
< 0.1%
9.51
< 0.1%
111
< 0.1%
11.8751
< 0.1%
ValueCountFrequency (%)
40498.51
< 0.1%
6009.3333331
< 0.1%
3868.651
< 0.1%
28801
< 0.1%
2733.9444441
< 0.1%
2518.7692311
< 0.1%
2160.3333331
< 0.1%
2082.2258061
< 0.1%
20001
< 0.1%
1903.51
< 0.1%

avg_unique_basket_size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct997
Distinct (%)35.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.12196419
Minimum1
Maximum299.7058824
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2022-08-07T08:45:34.387089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.5
Q110.12708333
median17.2967033
Q328
95-th percentile56.6469697
Maximum299.7058824
Range298.7058824
Interquartile range (IQR)17.87291667

Descriptive statistics

Standard deviation18.86759007
Coefficient of variation (CV)0.8528894591
Kurtosis24.17737545
Mean22.12196419
Median Absolute Deviation (MAD)8.296703297
Skewness3.158633785
Sum61366.32865
Variance355.9859551
MonotonicityNot monotonic
2022-08-07T08:45:34.721081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1344
 
1.6%
1430
 
1.1%
1129
 
1.0%
126
 
0.9%
926
 
0.9%
10.525
 
0.9%
7.525
 
0.9%
9.524
 
0.9%
17.524
 
0.9%
15.523
 
0.8%
Other values (987)2498
90.1%
ValueCountFrequency (%)
126
0.9%
1.21
 
< 0.1%
1.251
 
< 0.1%
1.3333333332
 
0.1%
1.58
 
0.3%
1.5681818181
 
< 0.1%
1.5714285711
 
< 0.1%
1.6666666674
 
0.1%
1.8333333331
 
< 0.1%
221
0.8%
ValueCountFrequency (%)
299.70588241
< 0.1%
203.51
< 0.1%
1451
< 0.1%
136.1251
< 0.1%
135.51
< 0.1%
1221
< 0.1%
1181
< 0.1%
1141
< 0.1%
110.33333331
< 0.1%
1101
< 0.1%

Interactions

2022-08-07T08:45:22.397490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:39.976781image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:43.365353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:46.428658image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:49.632347image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:53.046237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:56.418414image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:01.466867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:05.165616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:08.382928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:11.763485image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:15.170208image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:19.063267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:22.629008image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:40.216066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:43.608853image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:46.658312image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:49.847256image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:53.296281image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:56.672687image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:01.742430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:05.416873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:08.633158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:11.999987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:15.437266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:19.313170image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:22.861271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:40.444711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:43.820125image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:46.902379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:50.192689image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:53.535026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:56.936623image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:01.994218image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:05.658799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:08.891517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:12.254975image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:15.670387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:19.606673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:23.106521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:40.763199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:44.038709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:47.133021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:50.443632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:53.773224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:57.241878image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:02.251510image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:05.858539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:09.132700image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:12.502965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:15.883994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:19.832760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:23.349835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:41.028534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:44.278458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:47.370172image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:50.709678image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:54.006495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:57.526819image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:02.534035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:06.119618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:09.408405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:12.765154image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:16.166382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:20.091767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:23.609839image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:41.407060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:44.541244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:47.610857image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:50.966665image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:54.286689image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:57.788314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:02.811540image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:06.380290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:09.664105image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:13.037524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:16.453948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:20.359618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:23.831208image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:41.671729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:44.778734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:47.823742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:51.226548image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:54.545915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:58.037711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:03.090148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:06.629702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:09.900737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:13.328612image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:16.749885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:20.630648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:24.077218image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:41.907630image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:45.029490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:48.110263image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:51.499392image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:54.843692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:58.327774image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:03.399742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:06.887107image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:10.191062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:13.611217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:17.031528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:20.902139image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:24.308886image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:42.141747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:45.253501image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:48.341919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:51.720195image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:55.115631image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:59.589230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:03.669485image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:07.129928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:10.443694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:13.831190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:17.282666image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:21.139330image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:24.547909image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:42.384853image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:45.482015image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:48.592605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:51.952931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:55.384916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:59.850795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:03.905909image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:07.397954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:10.705289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:14.077422image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:17.571246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:21.400325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:24.794342image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:42.635447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:45.707836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:48.842724image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:52.234512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:55.645968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:00.123341image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:04.253324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:07.656509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:10.964637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:14.349505image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:17.808900image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:21.655803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:25.053256image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:42.881846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:45.928579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:49.103895image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:52.510500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:55.892410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:00.429759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:04.590048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:07.882062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:11.244018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:14.635105image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:18.077590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:21.893205image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:25.310154image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:43.127190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:46.185505image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:49.379502image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:52.777666image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:44:56.161411image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:01.141138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:04.885651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:08.130612image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:11.519004image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:14.910489image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:18.352690image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T08:45:22.155041image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-08-07T08:45:35.146066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-08-07T08:45:35.491636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-08-07T08:45:36.011967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-08-07T08:45:36.438234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-08-07T08:45:25.678935image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-08-07T08:45:26.175646image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexcustomer_idgross_revenuerecency_daysqtt_invoicesunique_productstotal_productsavg_ticketavg_recencydaily_purchase_ratetotal_prod_returnedavg_basket_sizeavg_unique_basket_size
00178505391.21372.034.021.01733.018.1522221.00000017.00000040.050.9705888.735294
11130473232.5956.09.0105.01390.018.90403552.8333330.02830235.0154.44444419.000000
22125836705.382.015.0114.05028.028.90250026.5000000.04032350.0335.20000015.466667
3313748948.2595.05.024.0439.033.86607192.6666670.0179210.087.8000005.600000
4415100876.00333.03.01.080.0292.00000020.0000000.07317122.026.6666671.000000
55152914623.3025.014.061.02102.045.32647126.7692310.04011529.0150.1428577.285714
66146885630.877.021.0148.03621.017.21978619.2631580.057221399.0172.42857115.571429
77178095411.9116.012.046.02057.088.71983639.6666670.03352041.0171.4166675.083333
881531160767.900.091.0567.038194.025.5434644.1910110.243316474.0419.71428626.142857
99160982005.6387.07.034.0613.029.93477647.6666670.0243900.087.5714299.571429

Last rows

df_indexcustomer_idgross_revenuerecency_daysqtt_invoicesunique_productstotal_productsavg_ticketavg_recencydaily_purchase_ratetotal_prod_returnedavg_basket_sizeavg_unique_basket_size
2764561117290525.243.02.092.0404.05.14941213.00.1428570.0202.00000051.0
276556201478577.4010.02.02.084.025.8000005.00.3333330.042.0000001.5
2766562117254272.444.02.0100.0252.02.43250011.00.1666670.0126.00000056.0
2767563717232421.522.02.030.0203.011.70888912.00.1538460.0101.50000018.0
2768563817468137.0010.02.05.0116.027.4000004.00.4000000.058.0000002.5
2769564913596697.045.02.0133.0406.04.1990367.00.2500000.0203.00000083.0
27705655148931237.859.02.072.0799.016.9568492.00.6666670.0399.50000036.5
2771568014126706.137.03.014.0508.047.0753333.00.75000050.0169.3333335.0
27725686135211092.391.03.0312.0733.02.5112414.50.3000000.0244.333333145.0
2773569615060301.848.04.080.0262.02.5153331.02.0000000.065.50000030.0